Best Practices in Statistical Process Monitoring of Biopharmaceutical Manufacturing Operations

Transcription

1 IBC Process2Product Oct 4th, 2011 Best Practices in Statistical Process Monitoring of Biopharmaceutical Manufacturing Operations Amer Pompe disease Jack Prior Sr. Director, BioProcess Engineering Technical Operations

2 BPOG Out of Trend/Statistical Process Control Working Group Steve Jones (BPOG) Bill Henry (GSK) Kevin Legg (Genzyme) Brian Stamper (MedImmune) Janet Alvarado (Merck) Monica Jungen (Merck-Serono) Paul McCormac (Pfizer) Christoph Hoh (Sanofi) Dave Hopkins (Genzyme) Jarrod Medeiros (Merck-Serono) Michael Warncke (Bayer) Cenk Undey (Amgen)

3 Origins: BPOG Data Analysis Working Group BioPhorum Operations Group (BPOG) 13 companies & ~300 participants Collaborating on biopharmaceutical MFG challenges Point Share organized on Data Analysis (October 2010, Boston, MA) Case studies/discussion: process monitoring, platforms/tools, organizations Point Share Observations Struggling with similar challenges Diverse approaches in place and contemplated Companies in process of defining SPC practices & standards Follow-up Focus on SPC and OOT Teleconferences Internal surveys Best practice sharing How can we summarize.process2product

4 Converging Drivers for Formal Statistical Process Monitoring Business Drivers Technical Support Mission cgmp Requirements Lean Avoid Surprises Preempt OOS Global supply chains CMO/tech transfers Biosimilars QbD Understand Control Improve Continuous Process Verification ICH Q10 Cost 2011

5 SPC Driver - FDA Validation Guidance: Continued Process Verification Continually assure the commercial process remains in a state of control Establish ongoing program to analyze product and process data Recommends Data statistically trended and reviewed by trained personnel Statisticians/equivalent with adequate SPC training develop plans Quality unit review

6 SPC Driver - ICH Q10: Pharmaceutical Quality System plan and execute system for monitoring of process performance and product quality Analyze parameters and attributes identified in control strategy to verify continued operation within a state of control continued capability of processes and controls data management and statistical tools identify sources of variation for continuous improvement enrich the design space

7 Goals of Effective SPC Program Efficient and proactive management of process o Enhance quality compliance and process understanding o Preempt deviations, don t generate them Balanced focus on process parameters and analytics o Focus effort on critical areas o Reduce effort on non-critical items Have the appropriate people looking at the appropriate data analyzed in the appropriate way responding in the appropriate manner, Kevin Legg, Genzyme

8 Key Questions Raised in Working Group on SPC implementation Strategy 1. How to decide what to formally trend? 2. How to respond to OOT Signals? 3. How to manage OOT Response? 4. How to define out of trend? 5. How to set (and reset) limits and on what reference data? 6. How to deal with unusual baseline data? 7. How to choose what statistical rules to apply? 8. How to learn more?

9 1. How to Choose which Variables to Trend? Define Process Control Strategy (PCS) 1 Break process into defined steps with defined purposes (the why vs. how ) Classify Process Control Elements (CPP, KPP, CQA, CI, PI ) PCS shapes monitoring strategy & signal response Formally trend/monitor CQAs (IPCs, ISs, Specs) and CPPs Set proportionate formality/response for process and consistency indicators 1 A planned set of controls, derived from current product and process understanding that assures process performance and product quality. (ICH Q10). Paul MaCormac, Pfizer

10 2. How to Respond to OOT Signals: Specification, Action, and Statistical Control Limits Specification Limit: Voice of Customer Set based on process capability, regulatory expectations and product safety concerns Action Limits: PV Acceptance Criteria, Batch record limits Statistical Control Limit: Voice of Process +/- 3 for n> 15 and/or PpK 1.33 Set based on process characterization and/or large scale data Paul MaCormac

11 2. How to Respond to OOT Signals: Potential Map from PCS Classification to Signal Response Variable Acceptable Range Normal Operating Range Statistical Control Limits Attributes CQAs* Specification/IS In-Process Control (IPC) Consistency Indicator (later) Process Indicator (real-time) Critical Deviation Major Deviation deviation N/A N/A Technical review DCS Alarm OOT/ APR Review Immediate alert OOT / APR Review Immediate alert Technical review Immediate alert Aggregate as CI Potential MVA** Parameters Critical Process Parameter (CPP)* Key process parameter (highly controlled) Non-KPP(nKPP) (no step impact) Major Deviation deviation DCS Alarm DCS Alarm Technical review Potential MVA Technical review Potential MVA *Defined in ICH Q8 **Multivariate Analysis

12 3. How to Manage OOT Response? Survey comments QC Lab If no confirmed lab error, QC triggers deviation If lab error, initial result invalidated after new result obtained Lab error sometimes only detected in CI calculation (e.g. yield outliers) Process Parameters & Consistency Indicators Manufacturing, technical, or (sometimes) validation responsibility Immediately enter in quality system OR trigger discussion OOT below a set performance level can be agreed as non-concern If uncovered during APR, investigation may be launched Frequency of Data Review Automate/alert after each discrete observation if possible Cross-functional notification can facilitate resolution Periodic data reviews for continuous data (e.g. run reviews) Formal evaluation may not occur until APR

13 3. How to Manage OOT Response? Team Structure Basis for Extent of Investigation Risk assessment & variable classification Potential impact to process performance and product quality Upfront work in Process Control Strategy (PCS) guides actions

14 4. How to Define Out of Trend? Variation not explained by a single distribution of process output 1 Violation of the Western Electric or Nelson trending rules 2 Result from a process not associated with random variation Visual review...outside 3 discretion of lab management Can vary by site or group within site (i.e. QC vs. manufacturing) Consensus on +/- 3 limits but from what data set? 1. MJ Kiemele et al. Basic Statistics: Tools for Continuous Improvement.4 th Edition 2. NIST/SEMATECH e-handbook of Statistical Methods, / 04MAY2011

15 5. How and When to Define Control Limits? Choose right reference data set After ~15-30 batches from stable process (Shewhart recommends 25+) Use interim provisional limits from process validation criteria Use largest dataset representative of current process & testing Exclude Outliers Remove known root (special) causes Use objective outlier exclusion methods Use judgement -> tighter limits (conservative, defendable, appropriate?) Show outliers even if excluded from calculations Revisit/adapt when needed When known process or analytical change impacts mean or variability As APR recommendation or in change control Can shift mean but retain long term variance

16 6. How to Handle Unusual Data? Situation out of control historical data non-normal historical data LoQ assays (e.g. LaL, purity) Strategies/Comments Exclude special cause variability Incorporate all common cause variability Short horizon reference range often too narrow Utilize moving range for control limit calculations. 3 limits often apply 1 Avoid complex WE/Nelson rules Transform data prior to applying rules Visually inspect trends instead If sufficient data is available >LoQ, exclude <LoQ Otherwise, use percentile approach 1 Wheeler, Normality and the process behavior chart, SPC Press

17 7. When & How to use Western Electric/Nelson Rules? Detecting Common vs. Special Cause Variation Short Horizon Common Cause Assay noise Raw materials Procedural noise Long Horizon Common/Special Cause (Not visible in 25 points) Lab transfers Reagent lots Personnel Column packing Upstream productivity Plant utilization Special Cause Operator errors Equipment failures Sample handling Data typos/calibration Material instability Data rarely independent and identically distributed Real processes trend/move - resulting in false signals 3 and 2/3 outside 2 rules OK - complex rules often inappropriate Backward looking rules impact earlier lots Trend signals slow in bio environment (e.g. 6 batches in a row) Value of well-trained eyes on right parameters

18 8. How to Learn More? Regulatory Guidance ICH Q9 International Conference on Harmonization (ICH) Q10: Section 3.2 ICH Q11 (Draft): Section 3.2, Section 6.1 New Process Validation Guidance Statistical Process Control DS Chambers, D Wheeler, Understanding Statistical Process Control DC Montgomery, Introduction to statistical quality control. 6 th Ed. MJ Kiemele et al, Basic Statistics: Tools for Continuous Improvement. 4th Ed. NIST/SEMATECH e-handbook of Statistical Methods, / 04MAY2011 William A. Levinson, Statistical Process Control for Real-World Applications, CRC Press

19 Summary: Key Success Factors in SPC Implementation Infrastructure Data management to enable trending and ensure data integrity Appropriate state of control to enable decisions based on trended data Quality Systems & Business Processes Proper OOT definition, notification methods, and proportional response Defined responsible parties to ensure timely response to OOT Process Control Strategy Appropriate risk-based classification of parameters and attributes Statistical tools/methods to ensure right limits and rules applied Focus on preventing deviations Knowledge capture